



'''simple docstring'''



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_  :							Optional[Any]             =    logging.get_logger(__name__)
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Tuple						  =					['input_features', 'is_longer']







						def __init__(	self :   int					,     lowerCAmelCase__ :   List[str]=64					,     lowerCAmelCase__ :   Tuple=4_80_00					,     lowerCAmelCase__ :   Tuple=4_80					,     lowerCAmelCase__ :   Union[str, Any]=10					,     lowerCAmelCase__ :   List[Any]=10_24					,     lowerCAmelCase__ :   List[str]=0.0					,     lowerCAmelCase__ :   Optional[int]=False					,     lowerCAmelCase__ :   float = 0					,     lowerCAmelCase__ :   float = 1_40_00					,     lowerCAmelCase__ :   int = None					,     lowerCAmelCase__ :   str = "fusion"					,     lowerCAmelCase__ :   str = "repeatpad"					,     **lowerCAmelCase__ :   int					,     ):

													"""simple docstring"""
													super().__init__(
													    feature_size=lowerCAmelCase__					,     sampling_rate=lowerCAmelCase__					,     padding_value=lowerCAmelCase__					,     return_attention_mask=lowerCAmelCase__					,     **lowerCAmelCase__					,     )
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      top_db
													SCREAMING_SNAKE_CASE       :   List[Any]         =      truncation
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      padding
													SCREAMING_SNAKE_CASE       :   List[Any]         =      fft_window_size
													SCREAMING_SNAKE_CASE       :   Any         =      (fft_window_size >> 1) + 1
													SCREAMING_SNAKE_CASE       :   int         =      hop_length
													SCREAMING_SNAKE_CASE       :   Tuple         =      max_length_s
													SCREAMING_SNAKE_CASE       :   Tuple         =      max_length_s * sampling_rate
													SCREAMING_SNAKE_CASE       :   List[str]         =      sampling_rate
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      frequency_min
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      frequency_max
													SCREAMING_SNAKE_CASE       :   int         =      mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins					,     num_mel_filters=lowerCAmelCase__					,     min_frequency=lowerCAmelCase__					,     max_frequency=lowerCAmelCase__					,     sampling_rate=lowerCAmelCase__					,     norm=lowerCAmelCase__					,     mel_scale='''htk'''					,     )
													SCREAMING_SNAKE_CASE       :   Any         =      mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins					,     num_mel_filters=lowerCAmelCase__					,     min_frequency=lowerCAmelCase__					,     max_frequency=lowerCAmelCase__					,     sampling_rate=lowerCAmelCase__					,     norm='''slaney'''					,     mel_scale='''slaney'''					,     )







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      copy.deepcopy(self.__dict__					)
													SCREAMING_SNAKE_CASE       :   int         =      self.__class__.__name__
													if "mel_filters" in output:
																				del output["mel_filters"]
													if "mel_filters_slaney" in output:
																				del output["mel_filters_slaney"]
													return output







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   np.array					,     lowerCAmelCase__ :   Optional[np.array] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      spectrogram(
													    lowerCAmelCase__					,     window_function(self.fft_window_size					,     '''hann'''					)					,     frame_length=self.fft_window_size					,     hop_length=self.hop_length					,     power=2.0					,     mel_filters=lowerCAmelCase__					,     log_mel='''dB'''					,     )
													return log_mel_spectrogram.T







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.array_split(list(range(0					,     total_frames - chunk_frames + 1					)					)					,     3					)
													if len(ranges[1]					) == 0:
																				# if the audio is too short, we just use the first chunk
																				SCREAMING_SNAKE_CASE       :   int         =      [0]
													if len(ranges[2]					) == 0:
																				# if the audio is too short, we just use the first chunk
																				SCREAMING_SNAKE_CASE       :   Dict         =      [0]
													# randomly choose index for each part
													SCREAMING_SNAKE_CASE       :   List[Any]         =      np.random.choice(ranges[0]					)
													SCREAMING_SNAKE_CASE       :   Any         =      np.random.choice(ranges[1]					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.random.choice(ranges[2]					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      mel[idx_front : idx_front + chunk_frames, :]
													SCREAMING_SNAKE_CASE       :   Dict         =      mel[idx_middle : idx_middle + chunk_frames, :]
													SCREAMING_SNAKE_CASE       :   Dict         =      mel[idx_back : idx_back + chunk_frames, :]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.tensor(mel[None, None, :]					)
													SCREAMING_SNAKE_CASE       :   str         =      torch.nn.functional.interpolate(
													    lowerCAmelCase__					,     size=[chunk_frames, 64]					,     mode='''bilinear'''					,     align_corners=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Dict         =      mel_shrink[0][0].numpy()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,     axis=0					)
													return mel_fusion







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   np.array					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													if waveform.shape[0] > max_length:
																				if truncation == "rand_trunc":
																											SCREAMING_SNAKE_CASE       :   Tuple         =      True
																											# random crop to max_length (for compatibility) -> this should be handled by self.pad
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      len(lowerCAmelCase__					) - max_length
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.random.randint(0					,     overflow + 1					)
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      waveform[idx : idx + max_length]
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      self._np_extract_fbank_features(lowerCAmelCase__					,     self.mel_filters_slaney					)[None, :]
																				elif truncation == "fusion":
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self._np_extract_fbank_features(lowerCAmelCase__					,     self.mel_filters					)
																											SCREAMING_SNAKE_CASE       :   Any         =      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mel.shape[0]
																											if chunk_frames == total_frames:
																																		# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																		# In this case, we just use the whole audio.
																																		SCREAMING_SNAKE_CASE       :   int         =      np.stack([mel, mel, mel, mel]					,     axis=0					)
																																		SCREAMING_SNAKE_CASE       :   int         =      False
																											else:
																																		SCREAMING_SNAKE_CASE       :   Optional[int]         =      self._random_mel_fusion(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
																																		SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      True
																				else:
																											raise NotImplementedError(F"""data_truncating {truncation} not implemented"""					)

													else:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      False
																				# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																				if waveform.shape[0] < max_length:
																											if padding == "repeat":
																																		SCREAMING_SNAKE_CASE       :   str         =      int(max_length / len(lowerCAmelCase__					)					)
																																		SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.stack(np.tile(lowerCAmelCase__					,     n_repeat + 1					)					)[:max_length]
																											if padding == "repeatpad":
																																		SCREAMING_SNAKE_CASE       :   List[str]         =      int(max_length / len(lowerCAmelCase__					)					)
																																		SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.stack(np.tile(lowerCAmelCase__					,     lowerCAmelCase__					)					)
																											SCREAMING_SNAKE_CASE       :   Any         =      np.pad(lowerCAmelCase__					,     (0, max_length - waveform.shape[0])					,     mode='''constant'''					,     constant_values=0					)

																				if truncation == "fusion":
																											SCREAMING_SNAKE_CASE       :   List[str]         =      self._np_extract_fbank_features(lowerCAmelCase__					,     self.mel_filters					)
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      np.stack([input_mel, input_mel, input_mel, input_mel]					,     axis=0					)
																				else:
																											SCREAMING_SNAKE_CASE       :   List[str]         =      self._np_extract_fbank_features(lowerCAmelCase__					,     self.mel_filters_slaney					)[None, :]

													return input_mel, longer







						def __call__(	self :   int					,     lowerCAmelCase__ :   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,     lowerCAmelCase__ :   str = None					,     lowerCAmelCase__ :   Optional[str] = None					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   Optional[int] = None					,     lowerCAmelCase__ :   Optional[Union[str, TensorType]] = None					,     **lowerCAmelCase__ :   Any					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      truncation if truncation is not None else self.truncation
													SCREAMING_SNAKE_CASE       :   Tuple         =      padding if padding else self.padding

													if sampling_rate is not None:
																				if sampling_rate != self.sampling_rate:
																											raise ValueError(
																											    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																											    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																											    F""" was sampled with {self.sampling_rate} and not {sampling_rate}."""					)
													else:
																				logger.warning(
																				    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
																				    '''Failing to do so can result in silent errors that might be hard to debug.'''					)

													SCREAMING_SNAKE_CASE       :   str         =      isinstance(lowerCAmelCase__					,     np.ndarray					) and len(raw_speech.shape					) > 1
													if is_batched_numpy and len(raw_speech.shape					) > 2:
																				raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      is_batched_numpy or (
													    isinstance(lowerCAmelCase__					,     (list, tuple)					) and (isinstance(raw_speech[0]					,     (np.ndarray, tuple, list)					))
													)

													if is_batched:
																				SCREAMING_SNAKE_CASE       :   str         =      [np.asarray(lowerCAmelCase__					,     dtype=np.floataa					) for speech in raw_speech]
													elif not is_batched and not isinstance(lowerCAmelCase__					,     np.ndarray					):
																				SCREAMING_SNAKE_CASE       :   Any         =      np.asarray(lowerCAmelCase__					,     dtype=np.floataa					)
													elif isinstance(lowerCAmelCase__					,     np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																				SCREAMING_SNAKE_CASE       :   List[str]         =      raw_speech.astype(np.floataa					)

													# always return batch
													if not is_batched:
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      [np.asarray(lowerCAmelCase__					)]

													# convert to mel spectrogram, truncate and pad if needed.
													SCREAMING_SNAKE_CASE       :   str         =      [
													    self._get_input_mel(lowerCAmelCase__					,     max_length if max_length else self.nb_max_samples					,     lowerCAmelCase__					,     lowerCAmelCase__					)
													    for waveform in raw_speech
													]

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      []
													SCREAMING_SNAKE_CASE       :   List[Any]         =      []
													for mel, longer in padded_inputs:
																				input_mel.append(lowerCAmelCase__					)
																				is_longer.append(lowerCAmelCase__					)

													if truncation == "fusion" and sum(lowerCAmelCase__					) == 0:
																				# if no audio is longer than 10s, then randomly select one audio to be longer
																				SCREAMING_SNAKE_CASE       :   str         =      np.random.randint(0					,     len(lowerCAmelCase__					)					)
																				SCREAMING_SNAKE_CASE       :   Any         =      True

													if isinstance(input_mel[0]					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      [np.asarray(lowerCAmelCase__					,     dtype=np.floataa					) for feature in input_mel]

													# is_longer is a list of bool
													SCREAMING_SNAKE_CASE       :   str         =      [[longer] for longer in is_longer]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {'''input_features''': input_mel, '''is_longer''': is_longer}
													SCREAMING_SNAKE_CASE       :   Tuple         =      BatchFeature(lowerCAmelCase__					)

													if return_tensors is not None:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      input_features.convert_to_tensors(lowerCAmelCase__					)

													return input_features









'''simple docstring'''



import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_  :							Tuple             =    'docs/source/en/_toctree.yml'
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      defaultdict(A					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							SCREAMING_SNAKE_CASE       :   Tuple         =      [key for key, value in counts.items() if value > 1]

							SCREAMING_SNAKE_CASE       :   Tuple         =      []
							for duplicate_key in duplicates:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
														if len(A					) > 1:
																					raise ValueError(
																					    F"""{duplicate_key} is present several times in the documentation table of content at """
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''					)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

							# Sort
							return sorted(A       ,		key=lambda A					: s["title"].lower()					)
def        UpperCAmelCase (    A	:					Any=False					):
							with open(A       ,		encoding='''utf-8'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      yaml.safe_load(f.read()					)

							# Get to the API doc
							SCREAMING_SNAKE_CASE       :   Any         =      0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							SCREAMING_SNAKE_CASE       :   Dict         =      content[api_idx]['''sections''']

							# Then to the model doc
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							SCREAMING_SNAKE_CASE       :   Dict         =      api_doc[model_idx]['''sections''']

							SCREAMING_SNAKE_CASE       :   List[Any]         =      [(idx, section) for idx, section in enumerate(A					) if '''sections''' in section]
							SCREAMING_SNAKE_CASE       :   int         =      False
							for idx, modality_doc in modalities_docs:
														SCREAMING_SNAKE_CASE       :   List[str]         =      modality_doc['''sections''']
														SCREAMING_SNAKE_CASE       :   List[str]         =      clean_model_doc_toc(A					)

														if old_modality_doc != new_modality_doc:
																					SCREAMING_SNAKE_CASE       :   Dict         =      True
																					if overwrite:
																												SCREAMING_SNAKE_CASE       :   int         =      new_modality_doc

							if diff:
														if overwrite:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      model_doc
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      api_doc
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
																												f.write(yaml.dump(A       ,		allow_unicode=A					)					)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							lowerCAmelCase_  :							Any             =    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




